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Analysis using all of the nanostring data on the 7 biomarkers previously identified using our samples with known lactate levels at 3 hours of perfusion. The goal is to see if the samples with unknonw lactate levels fit our previously generated predictive model.




Multiple Linear Regression Predictive Model generated with original data set

## 
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 + 
##     GSTA2, data = mlm.data)
## 
## Residuals:
##        FV2        LV2        LV3        FV3        LV1        FN1        FN2 
## -1.7772858 -0.4726029 -0.6974732 -0.3509557  2.7526045  0.8175029 -0.3654356 
##        LN1        FN3        LN3 
## -0.0006994 -0.0181005  0.1124458 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.1919727  2.8772531  -1.457    0.282
## EPHX1        0.0007600  0.0016188   0.469    0.685
## TKT          0.0245085  0.0283383   0.865    0.478
## GPX2        -0.0013174  0.0018790  -0.701    0.556
## JUN          0.0082264  0.0293291   0.280    0.805
## CYP2B6      -0.0004416  0.0004110  -1.074    0.395
## GSTA1       -0.0001505  0.0001989  -0.757    0.528
## GSTA2        0.0013193  0.0010763   1.226    0.345
## 
## Residual standard error: 2.488 on 2 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.8926 
## F-statistic: 11.68 on 7 and 2 DF,  p-value: 0.0811




Applying the predictive model to all data points




The number of libraries that did not detect a specific gene




The correlation for each gene with lactate using all samples




Heatmap with data scaled by gene/row and clustered by sample/column

Functional data and DCD/DBD status is labeled above the columns







PCA using scaled data for all samples




PCA using scaled data for the known samples only

Multiple Linear Regression Predictive Model generated using all samples

## 
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + 
##     GSTA1 + GSTA2, data = mlm.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4583 -2.4911 -0.7999  1.5243  9.4388 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -1.080e+00  3.165e+00  -0.341   0.7406  
## EPHX1       -1.944e-03  1.240e-03  -1.568   0.1513  
## TKT          3.048e-02  1.193e-02   2.555   0.0309 *
## GPX2        -2.012e-05  1.240e-03  -0.016   0.9874  
## JUN          9.958e-04  3.370e-03   0.295   0.7743  
## CYP2B6       7.556e-05  3.593e-04   0.210   0.8381  
## GSTA1        1.466e-04  2.032e-04   0.721   0.4890  
## GSTA2        1.223e-03  7.761e-04   1.576   0.1496  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.661 on 9 degrees of freedom
## Multiple R-squared:  0.7791, Adjusted R-squared:  0.6073 
## F-statistic: 4.535 on 7 and 9 DF,  p-value: 0.01976




Applying the new predictive model to all of the data




Verifying model conditions




Estimated 3 hour lactate levels of all samples using the new predictive model